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Spearman Correlation of Models

Summary of 5_Default_NeuralNetwork
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Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 16
- learning_rate: 0.05
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
9.1 seconds
Metric details
|
score |
threshold |
| logloss |
0.498088 |
nan |
| auc |
0.869571 |
nan |
| f1 |
0.794915 |
0.61198 |
| accuracy |
0.84 |
0.6751 |
| precision |
1 |
1 |
| recall |
1 |
4.70116e-09 |
| mcc |
0.681178 |
0.6751 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.498088 |
nan |
| auc |
0.869571 |
nan |
| f1 |
0.793103 |
0.6751 |
| accuracy |
0.84 |
0.6751 |
| precision |
0.910891 |
0.6751 |
| recall |
0.70229 |
0.6751 |
| mcc |
0.681178 |
0.6751 |
Confusion matrix (at threshold=0.6751)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
800 |
45 |
| Labeled as 1 |
195 |
460 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of 1_Baseline
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Baseline Classifier (Baseline)
- n_jobs: -1
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
0.5 seconds
Metric details
|
score |
threshold |
| logloss |
0.685103 |
nan |
| auc |
0.5 |
nan |
| f1 |
0.607889 |
0.3928 |
| accuracy |
0.436667 |
0.3928 |
| precision |
0.436667 |
0.3928 |
| recall |
1 |
0.3928 |
| mcc |
0 |
0.3928 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.685103 |
nan |
| auc |
0.5 |
nan |
| f1 |
0.607889 |
0.3928 |
| accuracy |
0.436667 |
0.3928 |
| precision |
0.436667 |
0.3928 |
| recall |
1 |
0.3928 |
| mcc |
0 |
0.3928 |
Confusion matrix (at threshold=0.3928)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
0 |
845 |
| Labeled as 1 |
0 |
655 |
Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of Ensemble
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Ensemble structure
| Model |
Weight |
| 3_Linear |
1 |
| 4_Default_Xgboost |
1 |
| 5_Default_NeuralNetwork |
1 |
Metric details
|
score |
threshold |
| logloss |
0.395402 |
nan |
| auc |
0.88475 |
nan |
| f1 |
0.805071 |
0.439696 |
| accuracy |
0.842 |
0.519909 |
| precision |
1 |
0.973635 |
| recall |
1 |
0.00300079 |
| mcc |
0.685303 |
0.610451 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.395402 |
nan |
| auc |
0.88475 |
nan |
| f1 |
0.802005 |
0.519909 |
| accuracy |
0.842 |
0.519909 |
| precision |
0.885609 |
0.519909 |
| recall |
0.732824 |
0.519909 |
| mcc |
0.680847 |
0.519909 |
Confusion matrix (at threshold=0.519909)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
783 |
62 |
| Labeled as 1 |
175 |
480 |
Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of 2_DecisionTree
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Decision Tree
- n_jobs: -1
- criterion: gini
- max_depth: 3
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
8.6 seconds
Metric details
|
score |
threshold |
| logloss |
0.583441 |
nan |
| auc |
0.739429 |
nan |
| f1 |
0.656958 |
0.260268 |
| accuracy |
0.704667 |
0.581966 |
| precision |
0.746512 |
0.581966 |
| recall |
1 |
0.105973 |
| mcc |
0.396031 |
0.581966 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.583441 |
nan |
| auc |
0.739429 |
nan |
| f1 |
0.591705 |
0.581966 |
| accuracy |
0.704667 |
0.581966 |
| precision |
0.746512 |
0.581966 |
| recall |
0.490076 |
0.581966 |
| mcc |
0.396031 |
0.581966 |
Confusion matrix (at threshold=0.581966)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
736 |
109 |
| Labeled as 1 |
334 |
321 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of 6_Default_RandomForest
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Random Forest
- n_jobs: -1
- criterion: gini
- max_features: 0.9
- min_samples_split: 30
- max_depth: 4
- eval_metric_name: auc
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
24.0 seconds
Metric details
|
score |
threshold |
| logloss |
0.53895 |
nan |
| auc |
0.794363 |
nan |
| f1 |
0.695218 |
0.396989 |
| accuracy |
0.742667 |
0.59434 |
| precision |
0.958824 |
0.800951 |
| recall |
1 |
0.0745929 |
| mcc |
0.501343 |
0.626023 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.53895 |
nan |
| auc |
0.794363 |
nan |
| f1 |
0.613226 |
0.59434 |
| accuracy |
0.742667 |
0.59434 |
| precision |
0.892128 |
0.59434 |
| recall |
0.467176 |
0.59434 |
| mcc |
0.500005 |
0.59434 |
Confusion matrix (at threshold=0.59434)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
808 |
37 |
| Labeled as 1 |
349 |
306 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of 4_Default_Xgboost
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Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: binary:logistic
- eta: 0.075
- max_depth: 6
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: auc
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
286.5 seconds
Metric details
|
score |
threshold |
| logloss |
0.429753 |
nan |
| auc |
0.867461 |
nan |
| f1 |
0.780811 |
0.505597 |
| accuracy |
0.826667 |
0.562002 |
| precision |
1 |
0.931576 |
| recall |
1 |
0.00658826 |
| mcc |
0.654215 |
0.58899 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.429753 |
nan |
| auc |
0.867461 |
nan |
| f1 |
0.775087 |
0.562002 |
| accuracy |
0.826667 |
0.562002 |
| precision |
0.894212 |
0.562002 |
| recall |
0.683969 |
0.562002 |
| mcc |
0.653299 |
0.562002 |
Confusion matrix (at threshold=0.562002)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
792 |
53 |
| Labeled as 1 |
207 |
448 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of 3_Linear
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Logistic Regression (Linear)
- n_jobs: -1
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
14.2 seconds
Metric details
|
score |
threshold |
| logloss |
0.493363 |
nan |
| auc |
0.86439 |
nan |
| f1 |
0.775849 |
0.450781 |
| accuracy |
0.816667 |
0.645845 |
| precision |
0.969512 |
0.981788 |
| recall |
1 |
9.51433e-05 |
| mcc |
0.627684 |
0.645845 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.493363 |
nan |
| auc |
0.86439 |
nan |
| f1 |
0.770259 |
0.645845 |
| accuracy |
0.816667 |
0.645845 |
| precision |
0.850554 |
0.645845 |
| recall |
0.703817 |
0.645845 |
| mcc |
0.627684 |
0.645845 |
Confusion matrix (at threshold=0.645845)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
764 |
81 |
| Labeled as 1 |
194 |
461 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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